We use a reverse KL divergence to ensure that our inverse dynamics model has a broader distribution than the approximate posterior to ensure mode coverage [1]}. In our implementation we use a feed-forward network that takes in the concatenation of the current and future state and outputs the parameters of a Gaussian distribution over \(z\) .
Conditioning on the future enables us to make a more informative decision on what skills to execute which is a key enabler to few-shot imitation. We jointly optimize the skill extractions and inverse model with the following loss:
\(\mathcal {L}(\phi , \theta , \psi ) = \mathcal {L}_{\text{rec}}(\phi , \theta ) + \beta \mathcal {L}_{\text{reg}} (\phi )+ \mathcal {L}_{\text{prior}}(\psi )\) 
